Learning Conditional Acoustic Latent Representation with Gender and Age Attributes for Automatic Pain Level Recognition

Li, Jeng Lin; Weng, Yi Ming; Ng, Chau Yee; Lee, Chi Chun

doi:10.21437/interspeech.2018-1298

Cited by 8 publications

(17 citation statements)

References 26 publications

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“…Thiam et al [60], [61] analyzed the audio signals of the SenseEmotion database, which do not contain verbal interaction, but mostly breathing noises and sporadic moaning sounds. In contrast, Tsai et al [97], [98] and Li et al [84] analyzed audio signals recorded during clinical interviews in an emergency triage situation. Whereas audio outperformed video-based facial expression recognition in Tsai et al [97], the opposite results were found by Thiam et al [60].…”

Section: Audio Approachesmentioning

confidence: 99%

“…In the audio domain the most widely used features are Mel Frequency Cepstral Coefficients (MFCC) [60], [61], [69], [70], [91], [97], [98], a spectral representation of sound that approximates the human auditory system's response. Other features include pitch [68], [84], [91], [97], [98], intensity [84], [91], [97], [98], Relative Spectral Perceptual Linear Predictive (RASTA-PLP) coefficients [60], [61], [91], Linear Predictive Coding (LPC) coefficients [60], [70], [91], harmonic to noise ratio [98], and formants [68]. It is common to include the first and second order temporal derivatives of features [60], [61], [91], [97], [98].…”

Section: Audio Featuresmentioning

confidence: 99%

“…Several works apply a person-specific feature transformation, either by calculating features relative to a person-specific mean or neutral state (PFN in Table 7, not marked in Table 2 and 5) [31], [63], [76], [90], [103], [104] or by applying a person-specific feature standardization (PFS in Table 3 and 7, not marked in Table 5), i.e. subtracting the mean and dividing by the standard deviation (or similar) [38], [44], [45], [48], [54], [57], [58], [64], [65], [84], [97]. Chu et al [78] used a person and day specific feature standardization (PDFS) in their 7-day pain recognition experiment, since the physiological baseline signals are day dependent.…”

Section: Use Of Contextmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Werner

Lopez-Martinez

Walter³

et al. 2022

IEEE Trans. Affective Comput.

158

119

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Pain is a complex phenomenon, involving sensory and emotional experience, that is often poorly understood, especially in infants, anesthetized patients, and others who cannot speak. Technology supporting pain assessment has the potential to help reduce suffering; however, advances are needed before it can be adopted clinically. This survey paper assesses the state of the art and provides guidance for researchers to help make such advances. First, we overview pain's biological mechanisms, physiological and behavioral responses, emotional components, as well as assessment methods commonly used in the clinic. Next, we discuss the challenges hampering the development and validation of pain recognition technology, and we survey existing datasets together with evaluation methods. We then present an overview of all automated pain recognition publications indexed in the Web of Science as well as from the proceedings of the major conferences on biomedical informatics and artificial intelligence, to provide understanding of the current advances that have been made. We highlight progress in both non-contact and contact-based approaches, tools using face, voice, physiology, and multi-modal information, the importance of context, and discuss challenges that exist, including identification of ground truth. Finally, we identify underexplored areas such as chronic pain and connections to treatments, and describe promising opportunities for continued advances.

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Section: Audio Approachesmentioning

confidence: 99%

Section: Audio Featuresmentioning

confidence: 99%

Section: Use Of Contextmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Werner

Lopez-Martinez

Walter³

et al. 2022

IEEE Trans. Affective Comput.

158

119

View full text Add to dashboard Cite

show abstract

“…Further, voice cues may reveal a speaker's smoking habit: A linear relationship has been observed between the number of cigarettes smoked per day and certain voice features, allowing for speech-based smoker detection in a relatively early stage of the habit (<10 years) [30]. Recorded human sounds can also be used for the automatic recognition of physical pain levels [61] and the detection of sleep disorders like obstructive sleep apnea [19].…”

Section: Speaker Pathologymentioning

confidence: 99%

Privacy Implications of Voice and Speech Analysis – Information Disclosure by Inference

Kröger

Lutz

Raschke

2020

Privacy and Identity Management. Data for Better Living: AI and Privacy

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Internet-connected devices, such as smartphones, smartwatches, and laptops, have become ubiquitous in modern life, reaching ever deeper into our private spheres. Among the sensors most commonly found in such devices are microphones. While various privacy concerns related to microphone-equipped devices have been raised and thoroughly discussed, the threat of unexpected inferences from audio data remains largely overlooked. Drawing from literature of diverse disciplines, this paper presents an overview of sensitive pieces of information that can, with the help of advanced data analysis methods, be derived from human speech and other acoustic elements in recorded audio. In addition to the linguistic content of speech, a speaker's voice characteristics and manner of expression may implicitly contain a rich array of personal information, including cues to a speaker's biometric identity, personality, physical traits, geographical origin, emotions, level of intoxication and sleepiness, age, gender, and health condition. Even a person's socioeconomic status can be reflected in certain speech patterns. The findings compiled in this paper demonstrate that recent advances in voice and speech processing induce a new generation of privacy threats.

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“…These studies tend to focus more on the prosodic and spectral properties of speech. Furthermore, except for a recent work done by Li et al that integrated gender and age attributes as auxiliary information to improve the vocal-based pain-level recognition [14], little if any work has studied exactly how various clinical attributes interact with acoustic manifestation across different pain-levels.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Variability of Voice Quality and Pain Levels as a Function of Multiple Clinical Parameters

Hong

Weng³

et al. 2019

Interspeech 2019

Self Cite

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Pain is an internal construct with vocal manifestation that varies as a function of personal and clinical attributes. Understanding the vocal indicators of pain-levels is important in providing an objective analytic in clinical assessment and intervention. In this work, we focus on investigating the variability of voice quality as a function of multiple clinical parameters at different pain-levels, specifically for emergency room patients during triage. Their pain-induced pathological voice quality characteristics are naturally affected by an individual attributes such as age, gender and pain-sites. We conduct a detailed multivariate statistical analysis on a 181 unique patient's vocal quality using recordings of real triage sessions. Our analysis show several important insights, 1) voice quality only varies statistically with pain-levels when interacting effect from other clinical parameters is considered, 2) senior group shows a higher value of voicing probability and shimmer when experiencing severe pain, 3) patients with abdomen pain have a lower jitter and shimmer during severe pain that is different from patients experiencing musculoskeletal pathology, and 4) there could be a relationship between the variation in the voice quality and the neural pathway of pain as evident by interacting with the pain-site factor.

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Learning Conditional Acoustic Latent Representation with Gender and Age Attributes for Automatic Pain Level Recognition

Cited by 8 publications

References 26 publications

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Automatic Recognition Methods Supporting Pain Assessment: A Survey

Privacy Implications of Voice and Speech Analysis – Information Disclosure by Inference

Investigating the Variability of Voice Quality and Pain Levels as a Function of Multiple Clinical Parameters

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